Gao, Junbin

Antolovich, Michael

Kwan, Paul Hing

2008

A new iterative procedure for solving regression problems with the so-called LASSO penalty is proposed by using generative Bayesian modeling and inference. The algorithm produces the anticipated parsimonious or sparse regression models that generalize well on unseen data. The proposed algorithm is quite robust and there is no need to specify any model hyperparameters. A comparison with state-of-the-art methods for constructing sparse regression models such as the relevance vector machine (RVM) and the local regularization assisted orthogonal least squares regression (LROLS) is given.

AI 2008: advances in artificial intelligence : 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, December 1-5, 2008, p. 318-324

978-3-540-89377-6

https://hdl.handle.net/1959.11/2480

Springer

Lecture notes in artificial intelligence

Lecture notes in computer science

1

L1 LASSO and its Bayesian Inference

Conference Publication

Publication